Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

The specialised, focused library is developed on demand with the most recent virtual screening and parameter assessment technology, guided by the Receptor.AI drug discovery platform. This approach exceeds the capabilities of traditional methods and offers compounds with higher activity, selectivity, and safety.


We pick out particular compounds from an extensive virtual database of more than 60 billion molecules. The preparation and shipment of these compounds are facilitated by Reaxense.


The library includes a list of the most effective modulators, each annotated with 38 ADME-Tox and 32 physicochemical and drug-likeness parameters. Furthermore, each compound is shown with its optimal docking poses, affinity scores, and activity scores, offering a detailed summary.


We use our state-of-the-art dedicated workflow for designing focused libraries for enzymes.


 

Fig. 1. The screening workflow of Receptor.AI

It includes comprehensive molecular simulations of the catalytic and allosteric binding pockets and the ensemble virtual screening accounting for their conformational mobility. In the case of designing modulators, the structural changes induced by reaction intermediates are taken into account to leverage activity and selectivity.


Key features that set our library apart include:


  • The Receptor.AI platform integrates extensive information about the target protein, such as historical experiments, academic research, known ligands, and structural insights, thereby increasing the likelihood of identifying highly relevant compounds.

  • The platform’s sophisticated molecular simulations are designed to discover potential binding sites, ensuring that our focused library is optimal for the discovery of allosteric inhibitors and binders for cryptic pockets.

  • With over 50 customisable AI models, verified through extensive testing in commercial drug discovery and research, Receptor.AI is efficient, reliable, and precise. These models are essential in the production of our focused libraries.

  • Receptor.AI not only produces focused libraries but also provides full services and solutions at every stage of preclinical drug discovery, with a success-based pricing structure that aligns our interests with the success of your project.


PARTNER
Receptor.AI
 
UPACC
Q9NP73

UPID:
ALG13_HUMAN

ALTERNATIVE NAMES:
Asparagine-linked glycosylation 13 homolog; Glycosyltransferase 28 domain-containing protein 1; UDP-N-acetylglucosamine transferase subunit ALG13 homolog

ALTERNATIVE UPACC:
Q9NP73; B1AKD6; B1AKM1; B2R5L5; B7Z6J0; B7Z804; B7Z847; B7Z9A8; B7ZAJ1; B7ZB57; Q17RC3; Q5JXY9; Q9H5U8

BACKGROUND:
ALG13, known for its possible multifunctional enzyme capabilities, plays a significant role in the dolichol-linked oligosaccharide pathway for protein N-glycosylation. It is identified by several names, including Asparagine-linked glycosylation 13 homolog and Glycosyltransferase 28 domain-containing protein 1. The protein's activities are essential for the proper development and function of various biological systems.

THERAPEUTIC SIGNIFICANCE:
The association of ALG13 with Developmental and Epileptic Encephalopathy 36 highlights its importance in neurological development and function. This connection to DEE36, a condition marked by severe epilepsy and developmental delays, emphasizes the potential for targeting ALG13 in therapeutic interventions. Exploring ALG13's functions and mechanisms could lead to breakthroughs in treating DEE36 and similar disorders.

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